Nowadays, the advancement of drones is also factored in development a world surrounded by technologies. One aspects emphasized here difficulty controlling drone, and system developed still under full control users as well. Reinforcement Learning used to enable operate automatically, thus drone will learn next movement based on interaction between agent environment. Through this study, Q-Learnin...

The extension of reinforcement learning (RL) to large state space has inevitably encountered the problem of the curse of dimensionality. Improving the learning efficiency of the agent is much more important to the practical application of RL. Consider learning to optimally solve Markov decision problems in a particular domain, if the domain has particular characteristics that are attributable t...

A Mobile Ad-Hoc Network (MANET) is a collection of wireless mobile nodes forming a temporary network without using any centralized access point, infrastructure, or centralized administration. The proposed Adaptive Energy-efficient On-demand Distance Vector routing (AEODV) Protocol for MANETs that is based on the Ad-hoc On-demand Distance Vector (AODV). The AEODV makes significant reduction in t...

The Optimized Link State Routing Protocol is a popular proactive routing protocol used in wireless mesh networks. However, like many protocols, OLSR can suffer from inefficiencies and suboptimal performance certain network conditions. To address these issues, researchers have proposed using reinforcement learning algorithms to improve the decisions made by OLSR. This paper explores use of three...

Recent studies have shown the potential of Reinforcement Learning (RL) algorithms in tuning parameters Model Predictive Controllers (MPC), including weights cost function and unknown MPC model. However, a framework for easy straightforward implementation that allows training just few episodes overcoming need imposing extra constraints as required by state-of-the-art methods, is still missing. I...